Machine Vision and Applications

, Volume 28, Issue 3–4, pp 373–391 | Cite as

Evaluating contour segment descriptors

  • Cong YangEmail author
  • Oliver Tiebe
  • Kimiaki Shirahama
  • Ewa Łukasik
  • Marcin Grzegorzek
Original Paper


Contour segment (CS) is the fundamental element of partial boundaries or edges in shapes and images. So far, CS has been widely used in many applications, including object detection/matching and open curve matching. To increase the matching accuracy and efficiency, a variety of CS descriptors have been proposed. A CS descriptor is formed by a chain of boundary or edge points and is able to encode the geometric configuration of a CS. Because many different CS descriptors exist, a structured overview and quantitative evaluation are required in the context of CS matching. This paper assesses 27 CS descriptors in a structured way. Firstly, the analytical invariance properties of CS descriptors are explored with respect to scaling, rotation and transformation. Secondly, their distinctiveness is evaluated experimentally on three datasets. Lastly, their computation complexity is studied. Based on results, we find that both CS lengths and matching algorithms affect the CS matching performance while matching algorithms have higher affection. The results also reveal that, with different combinations of CS descriptors and matching algorithms, several requirements in terms of matching speed and accuracy can be fulfilled. Furthermore, a proper combination of CS descriptors can improve the matching accuracy over the individuals.


Contour segment Contour segment descriptor Open curve matching Object retrieval 



Research activities leading to this work have been supported by the China Scholarship Council (CSC) and the German Research Foundation (DFG) within the Research Training Group 1564 (GRK 1564).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Cong Yang
    • 1
    Email author
  • Oliver Tiebe
    • 1
  • Kimiaki Shirahama
    • 1
  • Ewa Łukasik
    • 2
  • Marcin Grzegorzek
    • 1
    • 3
  1. 1.Research Group for Pattern Recognition, Institute for Vision and GraphicsUniversity of SiegenSiegenGermany
  2. 2.Laboratory of Operational Research and Artificial IntelligenceInstitute of Computing Science, Poznan University of TechnologyPoznanPoland
  3. 3.Faculty of Informatics and CommunicationUniversity of Economics in KatowiceKatowicePoland

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